Sequential Change Detection
Sequential Change Detection is a statistical methodology for on-line change-point detection that sequentially analyzes data streams in real time to distinguish the presence of a structural break or distributional shift from stationary behavior by applying recursive decision rules to incoming observations.
209
Publications
10.3K
Citations
381
Authors
211
Institutions
Publications per year
2017–2026
44
Parents
209
1–5 of 209
381
8
h-index
University of Southern California, United States
8 publications · 561 citations
8
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8
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7
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7
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1–5 of 381
211
Princeton University
Princeton, United States
10 h-index
19 publications
1K citations
University of Illinois Urbana-Champaign
Urbana, United States
9 h-index
14 publications
508 citations
Stanford University
Stanford, United States
8 h-index
8 publications
1K citations
Columbia University
New York, United States
7 h-index
12 publications
593 citations
University of Southern California
Los Angeles, United States
7 h-index
13 publications
1.2K citations
Georgia Institute of Technology
Atlanta, United States
7 h-index
14 publications
519 citations
University of Patras
Pátrai, Greece
6 h-index
6 publications
225 citations
Hebrew University of Jerusalem
Jerusalem, Israel
6 h-index
7 publications
213 citations
1–8 of 211
13 publications · 1.1K citations
13
h-index
13 publications · 557 citations
13
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12 publications · 492 citations
12
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6 publications · 155 citations
6
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Repository · 5 publications · 111 citations
5
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